GPUQP: query co-processing using graphics processors

ABSTRACT

We present GPUQP, a relational query engine that employs both CPUs and GPUs (Graphics Processing Units) for in-memory query co-processing. GPUs are commodity processors traditionally designed for graphics applications. Recent research has shown that they can accelerate some database operations orders of magnitude over CPUs. So far, there has been little work on how GPUs can be programmed for heavy-duty database constructs, such as tree indexes and joins, and how well a full-fledged GPU query co-processor performs in comparison with their CPU counterparts. In this work, we explore the design decisions in using GPUs for query co-processing using both a graphics API and a general purpose programming model. We then demonstrate the processing flows as well as the performance results of our methods.

REFERENCES

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INDEX TERMS

Primary Classification:
  H. Information Systems
  H.2 DATABASE MANAGEMENT
      H.2.4 Systems
          Subjects: Query processing

Additional Classification:
  H. Information Systems
  H.2 DATABASE MANAGEMENT
      H.2.4 Systems
          Subjects: Relational databases; Parallel databases

General Terms:
Algorithms, Design, Experimentation, Measurement, Performance

Keywords:
graphics processing units, query processing